Meta-analysis suggests that metformin may reduce pre-eclampsia compared with insulin use during pregnancy
Bibliographic record
Abstract
Commentary on : Alqudah A, McKinley MC, McNally R et al Risk of pre-eclampsia in women taking metformin: a systematic review and meta-analysis. Diabet Med 2018;35:160–172. Pre-eclampsia occurs in 2%–5% of pregnancies and is an important cause of maternal and fetal morbidity and mortality. The risk of pre-eclampsia increases 2.9 fold in obese women and 4.47 fold in women with glucose intolerance, and is therefore of particular concern in these populations. Metformin has been studied in three populations during pregnancy: women with polycystic ovary syndrome (PCOS), women with glucose intolerance (gestational diabetes (GDM) and occasionally women with type 2 diabetes (T2DM)) and in obese pregnant women. However, the effect of metformin on pre-eclampsia remains unclear, with variable results in all three populations. In the two randomised controlled trials (RCTs) of metformin use in obese women, one showed a significant reduction in pre-eclampsia while the other showed no difference. Results in women with PCOS have been conflicting …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".